Changes in Bioactive Constituents in Black Rice Metabolites Under Different Processing Treatments

Abstract

In this study, liquid chromatography-mass spectrometry (LC-MS) was employed to conduct untargeted metabolomics analysis on black rice (BR), milled black rice (MBR), wet germinated black rice (WBR), and high-temperature and high-pressure-treated WBR (HTP-WBR). A total of 6988 positive ions and 7099 negative ions (multiple difference ≥1.2 or ≤0.8333, p < 0.05, and variable importance in projection ≥1) were isolated, and 98 and 100 differential metabolic pathways were identified between the different samples in the positive and negative ion modes, respectively. Distinctive variations in the metabolic compositions of BR, MBR, WBR, and HTP-WBR were observed. Flavonoids, fatty acids, lipids, phenylpropanoids, polyketides, benzenoids, and organooxygen were the dominant differential metabolites. Milling removed the majority of bran-associated bioactive components such as phenolic acids, anthocyanins, micronutrients, fatty acids, antioxidants, and dietary fiber. The germination process significantly reduced the number of flavonoids, polyketides, and lipid-related metabolites, while enzymatic activation notably increased the number of organic acids and amino acids. HTP treatment synergistically enhanced the content of heat-stable flavonoids and polyketides, while simultaneously promoting fatty acid β-oxidation. These findings establish novel theoretical foundations for optimizing processing methodologies and advancing functional characterization in black rice product development.

Keywords: black rice (BR); germination; high temperature and pressure (HTP); metabolomics; milling.

Figure 1

Classification of identified metabolites as per the KEGG database and HMDB.

Figure 2

PCA assay of BR, MBR, WBR, and HTP-WBR. Each point in the figure corresponds to a sample, and the varying colors denote different groups.

Figure 3

Differential ion cluster analysis graph of BR:MBR, BR:WBR, and HTP-WBR:WBR. Each row in the figure represents a differential ion, and each column represents a sample. Different colors indicate different intensities, and colors range from green to red, indicating strength from low to high. (A): positive ions; (B): negative ions.

Figure 3

Differential ion cluster analysis graph of BR:MBR, BR:WBR, and HTP-WBR:WBR. Each row in the figure represents a differential ion, and each column represents a sample. Different colors indicate different intensities, and colors range from green to red, indicating strength from low to high. (A): positive ions; (B): negative ions.

Figure 4

Major classifications of differential metabolites (the top seven classifications) enriched in the positive (pos) and negative (neg) ion modes: (a,b) differential numbers of metabolites in BR:MBR under positive and negative ion conditions; (c,d) differential numbers of metabolites in BR:WBR under positive and negative ion conditions; and (e,f) differential numbers of metabolites in WBR:HTP-WBR under positive and negative ion conditions.

Figure 5

The primary metabolic pathways based on KEGG database.

Figure 6

Volcano plots of the differential metabolites: (a,b) volcano plots of BR and MBR; (c,d) volcano plots of BR and WBR; and (e,f) volcano plots of WBR and HTP-WBR. Red dots: Significantly upregulated points (points satisfying p < 0.05 and fold change ≤1.5). Green dots: Significantly downregulated points (points satisfying p < 0.05 and fold change ≤0.67). Gray dots: Non-significant points (points that do not meet the above criteria).

Figure 6

Volcano plots of the differential metabolites: (a,b) volcano plots of BR and MBR; (c,d) volcano plots of BR and WBR; and (e,f) volcano plots of WBR and HTP-WBR. Red dots: Significantly upregulated points (points satisfying p < 0.05 and fold change ≤1.5). Green dots: Significantly downregulated points (points satisfying p < 0.05 and fold change ≤0.67). Gray dots: Non-significant points (points that do not meet the above criteria).